System and method for monitoring and analyzing internet traffic

ABSTRACT

A system and method for monitoring and analyzing Internet traffic is provided that is efficient, completely automated, and fast enough to handle the busiest websites on the Internet, processing data many times faster than existing systems. The system and method of the present invention processes data by reading log files produced by web servers, or by interfacing with the web server in real time, processing the data as it occurs. The system and method of the present invention can be applied to one website or thousands of websites, whether they reside on one server or multiple servers. The multi-site and sub-reporting capabilities of the system and method of the present invention makes it applicable to servers containing thousands of websites and entire on-line communities. In one embodiment, the system and method of the present invention includes e-commerce analysis and reporting functionality, in which data from standard traffic logs is received and merged with data from e-commerce systems. The system and method of the present invention can produce reports showing detailed “return on investment” information, including identifying which banner ads, referrals, domains, etc. are producing specific dollars.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to Internet traffic and, morespecifically, to a system and method for monitoring and analyzingInternet traffic.

[0003] 2. Description of Related Art

[0004] Internet web servers such as those used by Internet ServiceProviders (ISP), are typically configured to keep a log of server usageby the on-line community. For example, as a visitor to a website clickson various hyperlinks and travels through a website, each step isrecorded by the web server in a log. Each web page, image and multimediafile viewed by the visitor, as well as each form submitted, may berecorded in the log.

[0005] The type of information logged generally includes the InternetProtocol (IP) address or host name of the visitor, the time of thetransaction, the request, the referring page, the web browser and typeof platform used by the visitor, and how much data was transferred. Whenproperly analyzed, this information can help marketing executives,webmasters, system administrators, business owners, or others makecritical marketing, business, commerce and technical decisions. The datacan be mined for all types of decision supporting information, e.g.analyzing which webbrowsers people are using, determining which bannerads are producing the most traffic, etc.

[0006] A problem with mining the raw log data for useful information isthe shear volume of data that is logged each day. ISPs may have dozensof web servers containing thousands of websites that produce gigabytesof data each day. Providing a robust system that can be used on variousplatforms, that can efficiently process the huge amounts of data thatare logged, and that can produce easy to use reports for each website inan automated fashion is a daunting task.

BRIEF SUMMARY OF THE INVENTION

[0007] In view of the above problems in the art, the present inventionprovides a system and method for monitoring and analyzing Internettraffic that is efficient, completely automated, and fast enough tohandle the busiest websites on the Internet, processing data many timesfaster than existing systems.

[0008] The system and method of the present invention processes data byreading log files produced by web servers, or by interfacing with theweb server in real time, processing the data as it occurs. The systemand method of the present invention can be applied to one website orthousands of websites, whether they reside on one server or multipleservers. The multi-site and sub-reporting capabilities of the system andmethod of the present invention makes it applicable to serverscontaining thousands of websites and entire on-line communities.

[0009] The system and method of the present invention can create reportsfor individual websites, as well as reports for all of the websitesresiding on a single server or multiple server. The system can alsocreate reports from a centralized system, in which reports are deliveredupon request directly from the system database via a Common GatewayInterface (CGI).

[0010] The system and method of the present invention can also includereal-time analysis and reporting functionality in which data from webservers is processed as it occurs. The system and method of the presentinvention can produce animated reports showing current activity on theweb server, which can be used by administrators and managers to monitorwebsite effectiveness and performance.

[0011] The system and method of the present invention can furtherinclude e-commerce analysis and reporting functionality in which datafrom standard traffic logs is received and merged with data frome-commerce systems. The system and method of the present invention canproduce reports showing detailed “return on investment” information,including identifying which banner ads, referrals, domains, etc. areproducing specific dollars.

[0012] The present invention can be achieved in whole or in part by asystem for analyzing and monitoring internet traffic, comprising arelational database, a log engine that processes log files received fromat least one internet server and stores data processed from the logfiles in the relational database; and a report engine that generatesreports based on the processed data stored in the relational database.The system and method of the present invention preferably utilizesVisitor Centric Data Modeling, which keeps data associated with thevisitor that generated it, and that allows for the cross-comparing ofdifferent elements of data coming from different log entries ordifferent log files altogether.

[0013] The accompanying drawings, which are incorporated in andconstitute a part of this specification, illustrates embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014]FIG. 1 is a schematic diagram of a system for monitoring andanalyzing Internet traffic, in accordance with the present invention;

[0015]FIG. 2 is a schematic diagram of a series of hash tables stored bythe database shown in FIG. 1;

[0016]FIG. 3 is a block diagram of a preferred embodiment of the logengine shown in FIG. 1;

[0017]FIG. 4 is a flowchart and schematic diagram illustrating apreferred control routine for the log parser module of FIG. 3;

[0018]FIG. 5 is a flowchart and schematic diagram of a preferred controlroutine for the read line step of FIG. 4, for accessing and processinglog file data in real time;

[0019]FIG. 6 is a flowchart and schematic diagram illustrating apreferred control routine for the website identification module of FIG.3;

[0020]FIG. 7 is a flowchart and schematic diagram illustrating apreferred control routine for the visitor identification module of FIG.3;

[0021]FIG. 8 is a flowchart and schematic diagram illustrating apreferred control routine for the buffer update module of FIG. 3;

[0022]FIG. 9 is a schematic representation of the contents of thedatabase buffer shown in FIG. 3;

[0023]FIG. 10 is a schematic diagram illustrating the operation of theDNS resolver module of FIG. 3;

[0024]FIG. 11 is a flowchart and schematic diagram of a feedback loopcontrol routine preferably used by the DNS resolver module of FIG. 3;

[0025]FIG. 12 is a schematic diagram of how a preferred embodiment of anadaptable resolution mechanism in the DNS resolver module operates;

[0026]FIG. 13 is a flowchart of preferred control routines for variouscontrol loops within the DNS resolver module of FIG. 3;

[0027]FIG. 14 is a flowchart and schematic diagram illustrating apreferred control routine for the database update module of FIG. 3;

[0028]FIG. 15 is a schematic diagram illustrating the main components ofthe database shown in FIG. 1;

[0029]FIG. 16 is a schematic diagram of a preferred embodiment of thereport engine of FIG. 1;

[0030]FIG. 17 is a flowchart of a preferred control routine for thesession parser module of FIG. 16;

[0031]FIG. 18 is a flowchart of a preferred control routine for theauthentication module of FIG. 16;

[0032]FIG. 19 is a flowchart of a preferred control routine for the dataquery module of FIG. 16;

[0033]FIG. 20 is a flowchart of a preferred control routine for theformat output module of FIG. 16;

[0034]FIG. 21 is a schematic diagram of a preferred embodiment of aJavascript system used by the report engine of FIG. 16;

[0035]FIG. 22 is an example of a visitor monitor report created by thesystem of the present invention;

[0036]FIG. 23 is an example of a temporal visitor drill down reportcreated by the system of the present invention;

[0037]FIG. 24 is an example of a visitor footprint report created by thesystem of the present invention;

[0038]FIG. 25 illustrates an example of a system meter report created bythe system of the present invention;

[0039]FIG. 26 shows visitor table containing e-commerce data, andresiding in the database buffer;

[0040]FIG. 27 shows an example of an ROIR e-commerce report generated bythe system of the present invention;

[0041]FIG. 28 shows an example of a snapshot report generated by thesystem of the present invention;

[0042]FIG. 29 shows an example of a user interface and an hourly graphreport generated by the system of the present invention;

[0043]FIG. 30 shows an example of a top pages report generated by thesystem of the present invention;

[0044]FIG. 31 shows an example of a directory tree report generated bythe system of the present invention;

[0045]FIG. 32 shows an example of a search engines report generated bythe system of the present invention;

[0046]FIG. 33 shows an example of a top domains report generated by thesystem of the present invention;

[0047]FIG. 34 shows an example of a browser tree report generated by thesystem of the present invention;

[0048]FIG. 35 shows an example of a top entrances report generated bythe system of the present invention; and

[0049]FIG. 36 shows an example of a top products report generated by thesystem of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0050]FIG. 1 illustrates a system 100 for monitoring and analyzingInternet traffic, in accordance with the present invention. The system100 comprises a log engine 200, a database 300 and a report engine 400.

[0051] In operation, log files 510 generated by web servers 500 are sentto the log engine 200. Web (Internet) traffic is served by the webserver 500. The web server 500 can host one or many individual websites.As visitors access the web servers 500 for content, each website hit ortransaction is appended to a log. Each web server will typically haveits own log file. Multiple websites on a single server could be loggedcentrally in one log file, or could be configured so that each websitehas its own log file. The system 100 is able to handle all of thesedifferent architectures.

[0052] The entries on each of the log files 510 are interleaved so thatindividual website hits or transactions are recorded in the order theyare received. If a single log file contains log entries from multiplewebsites, the log entries are also interleaved so that individual hitsor transactions from each website are recorded in the order they arereceived. Each line in the log files 510 represents a hit or atransaction from the website on one of the web servers 500.

[0053] In addition to normal web traffic, many websites containe-commerce enabled virtual “shopping carts” that allow visitors tosecurely buy products directly from the website. The system 100 canoptionally analyze the demographics of on-line shopping by receivinge-commerce log files 580 produced by e-commerce enabled websites. Thee-commerce log files 580 are transaction logs that contain informationabout each order placed on the website. Each of the e-commerce log files580 generally contains data on the pricing of products purchased, dollaramounts and shipping regions. Sensitive information such as creditnumbers, individual names and e-mail addresses are generally not storedon the e-commerce log files 580. Dashed lines are used to represent thee-commerce log files 580 to indicate that the e-commerce functionalityis an optional feature of the system 100.

[0054] The preferred embodiment of the log engine 200 is responsible forprocessing all of the log files 510 and 580, domain name system (DNS)resolving and updating the database 300. The log engine 200 utilizesmemory buffers, fixed-width data models and other techniques toefficiently process the log files 510 and 580. In addition, the logengine 200 can be optionally configured to access live data. Theoperation of the log engine 200 will be described in more detail below.

[0055] The log engine 200 efficiently reads each line in each of the logfiles 510 and separates each line into its individual parts. Theindividual parts can include fields such as the IP address, time stamp,bites sent, status code, referral, etc. The log engine 200 utilizes atechnique called Visitor Centric Data Modeling. Rather than parsing eachlog line and counting how many of one type of browser was used or howmany times a particular webpage was viewed, Visitor Centric DataModeling keeps that data associated with the visitor that generated it.One of the primary advantages of Visitor Centric Data Modeling is theability to cross compare different elements of data coming fromdifferent log entries or different log files altogether. Visitor CentricData Modeling allows one to determine what percentage of users thatoriginated from a Yahoo™ search looked at a particular webpage.

[0056] A second benefit of Visitor Centric Data Modeling is reduction ofoverall data processing. Because many elements of the data will be thesame during a visitor's visit, the information only needs to beprocessed once per visitor, rather than once per log line. For example,the primary domain name of the visitor will be the same for each logentry produced by a particular visitor. Visitor Centric Modeling allowsone to process this information only once per visitor. Additionaldetails on how the log engine 200 utilizes the Visitor Centric DataModeling will be provided below.

[0057] The log engine 200 processes each log entry and updates thedatabase 300. The database 300 contains a series of hash tables. Thedatabase 300 comprises a series of hash tables, as shown in FIG. 2. Thehash tables comprise a visitor table 310 and associated data tables 315.

[0058] The visitor table 310 contains the central record for eachvisitor to a website. The hits, bytes, page views, and other fixed dataparameters (hereinafter collectively referred to as “trafficinformation”) are stored directly in the visitor table 310. Theremaining non-unique parameters, e.g., domain names, types of webbrowsers, referring web sites, etc., are stored relationally inrespective data tables 315. For example, one of the data tables 315could be configured to store a list of the different domain names fromwhich the visitors to the website being monitored by the system 100originate, while another of the data tables 315 could be configured tostore the names of the different types of web browsers used by thevisitors to the web site being monitored by the system 100.

[0059] The database 300 is relational and centers the data in thevisitor table 310, creating a Visitor Centric Data Model. The visitortable 310 contains a hash table 320 that is used for quickly seekingvisitor records. Below the hash table 310, the actual records 325contain the traffic information of each visitor. Each unique visitorwill have their own record in the visitor table 310.

[0060] The visitor table 310 is relational in nature and has a relationsarea 330 that contains pointers 335 to records 350 within the datatables 315. As discussed above, each of these data tables 315 storedifferent visitor parameters such as domain, browser, and referral.Besides vastly reducing the storage requirements relative to anon-relational database, the data tables 315 can be used to createstatistical reports on the usage of different visitor parameters.

[0061] Each data table 315 contains a hash table 340, a rank table 345,a record table 350, and a string table 355. The hash table 340 is usedto seek records in the record table 350. The rank table 345 is used tokeep track of the top entries in the record table 350 based on thenumber of pointers 335 set to the records in the record table 350. Thisis useful for quick access to reports. The record table 350 stores theactual records within the data table 315 including the trafficinformation associated with the parameter associated with the data table315. The record table 350 does not store the value of the parameter.Instead, the record table 350 contains a pointer to a record in thestring table 355. Each of these subtables (320, 325, 340, 345, 350, 355)has fixed width records allowing for efficient reading, writing, andcopying of the entire data sets.

[0062] The relational structure of the database 300 has at least twoadvantages. First, the visitor table 310 simplifies the task ofprocessing each hit because, once the visitor is identified, theappropriate visitor table 310 can be identified and updated accordingly.Second, the data tables 315 simplify the task of report generation,because each of the data tables 315 stores a specific parameter (e.g.,the names of the web browsers used by the visitors) and are ranked.Thus, each of the data tables 315 can easily deliver the top list ofentries for a particular report.

[0063] Referring back to FIG. 1, once the log files 510, and optionallythe e-commerce log files 580, are processed by the log engine 200, andthe database 300 is updated, the system 100 is ready to deliver reportsbased on the updated information in the database 300. A user 530 sends areport request 540 to the report engine 400 via a web server 520. Thereport engine 400 obtains the data required to generate the report fromthe database 300, generates the report, and delivers the generatedreport 550 to the user 530 via the web server 520.

[0064] The web server 520 can optionally be one of the web servers 500that created the log files 510 and 580. The report engine 400 preferablyutilizes javascript application techniques, dictionaries, and templatesto provide flexible, efficient, customizable and attractive reports, aswill be explained in more detail below. Reports are generated on the flywhen requested by the user 530 using the standard Common GatewayInterface (CGI) of the web server 520. Storage requirements are keptsmall as all HTML and graphics for the reports are generated as needed.

Log Engine (200)

[0065]FIG. 3 is a block diagram of a preferred embodiment of the logengine 200. The log engine preferably comprises a log parser module 210,a website identification module 220, a visitor identification module230, a buffer update module 240, a DNS resolver module 250, a databasebuffer 260 and a database update module 270.

[0066] The log parser module 210 is responsible for the actual readingand processing of the log files 510 and the e-commerce log files 580.The log parser module 210 can be configured to process either static logfiles or log files that are being generated live in real-time. The logparser module 210 loads each log line from the log files 510 and 580 andseparates each log line into its individual fields.

[0067] The website identification module 220 is primarily used whenmultiple websites are being logged to the same file. A class of webhosting known as “virtual hosting” or “shared hosting” allows ISPs tooffer solid performing website hosting service at reasonable prices. Bysetting up a robust set of servers with virtual hosting capablesoftware, ISPs can place multiple websites on the servers, thus allowingthe website owners to share the cost of the servers, maintenance, andnetworking.

[0068] However, as ISPs squeeze more and more websites onto a server inorder to generate profit in an ever increasingly competitive industry,creating a system that is scalable becomes more and more difficult. Oneproblem that administrators soon face is the number log files openduring operation. Typically they will have at least one log file 510 foreach website. As they add hundreds or thousands of websites to a server,the handling of all log files 510 becomes difficult. Moving, rotatingand archiving all of the individual log files 510 becomes a burden.Also, system performance is compromised as resources are allocated toeach open log file (many systems have a hard limit to the number offiles that can be open simultaneously).

[0069] To solve this problem, the system and method of the presentinvention utilizes Subreport/Multisite Reporting Technology. Thistechnology allows hosting providers to centralize the logging for allwebsites. Each server can have just one log file 510 for all websites,keeping resources in check. There is just one log file 510 to manage,rotate, process and archive, thus making the administrator's dutieseasier, less expensive and more scalable.

[0070] This website identification module 220 identifies each hit asbelonging to a particular website. If the log file 510 or e-commerce logfile 580 has data from only one website, then the task is simple and ishandled through straight configuration. However, if the log file 510 ore-commerce log file 580 contains data from multiple websites, then thewebsite identification module 220 employs a series of regular expressionfilters to perform the website identification. The websiteidentification module 220 must be flexible and be able to pull anyconsistent part of the log file 510 for website identification. Thewebsite identification performed by the website identification module islater used to determine what portion of the database 300 to write thedata to.

[0071] As discussed above, the log engine 200 utilizes Visitor CentricData Modeling. The first step in using a Visitor Centric Data Model isto be able to identify the specific visitor within each log file line.The visitor identification module 230 analyzes the fields in each hit(log file line) and identifies the hit as belonging to a new or existingvisitor. Based on a unique identifier, such as an IP number or sessionid and a timestamp, the visitor identification module 230 determineswhich visitor record in the database 300 will need to be updated. If thetimestamp of the hit is within a predetermined amount of time (e.g., 30minutes) of an existing visitor, then the hit is considered as comingfrom that visitor.

[0072] The buffer update module 240 updates the parameters of thevisitor record found by the visitor identification module 230 and storedon the database buffer 250 with the current hit's information. Thetimestamp of the hit is used to keep the chronological order of eventsintact.

[0073] The database buffer 250 is a volatile storage area, preferablyRAM memory, that mirrors the actual database 300. At the beginning ofprocessing, current data is read from the database 300 into the databasebuffer 250. After processing is complete, data is written back to thedatabase 300. The purpose of the database buffer 250 is to speed up theprocessing of each hit. Instead of accessing the actual database 300 foreach hit in the log file 510 or e-commerce log file 580, the databasebuffer 250 allows the log engine 200 to build up the data in the fasterRAM memory location of the database buffer 250 and then flush data tothe database 300 in larger chunks. The operation of the database buffer250 will be explained in more detail below.

[0074] Before outputting the data to the database 300, the data ispassed through the DNS resolver module 260 for reverse DNS resolution ofIP addresses. Most web servers log only the IP address of the visitorand not the host and domain information. The domain information providesvaluable data about the physical and network location of visitors. TheDNS resolver module 260 employs a customized resolution routine designedspecifically to speed up the process of typically slow DNS operations.

[0075] The database update module 270 performs the task of updating thedatabase with the contents of the database buffer 260. The databaseupdate module 270 performs some processing (e.g., visitor sorting)before writing to the database 300.

[0076] Preferred control routines for the log parser module 210, websiteidentification module 220, visitor identification module 230, bufferupdate module 240, DNS resolver module 260 and database update module270 will be described below.

[0077] Log Parser Module (210)

[0078]FIG. 4 is a flowchart and schematic diagram illustrating apreferred control routine for the log parser module 210 of FIG. 3,configured to process static log files 510. One of the most timeconsuming operations is reading and processing the raw log files 510.With individual log files 510 containing potentially over a gigabyte ofdata, getting the raw data into the system 100 is an important step.

[0079] The purpose of the log parser module 210 is to efficiently readeach log line 512 and separate it into its individual fields. The fieldscan include the IP address, timestamp, bytes sent, status code,referral, etc. As discussed above, each log line 512 in the log file 510represents a hit or transaction from one of the web servers 500.

[0080] The log parser module 210 employs a log buffer 600 and a pointerarray 610 that is reused for each log line 512 in the log file 510.Thus, memory allocation for this log parser module 210 is only done atstartup. The states of the log buffer 600 and pointer array 610 at eachstep in the control routine shown in FIG. 4 are representedschematically under the corresponding step in the control routine.

[0081] The control routine starts at step 620, where the pre-allocatedlog buffer 600 and the pointer array 610 are cleared. The log buffer 600is cleared by setting the first character in the log buffer 600 to zero.The pointer array 610 is cleared by setting the values of all theindividual pointers 612 to zero. It is important for stable processingto set all of the pointers in the pointer array 610 to zero before usingthe pointer array 610.

[0082] The control routine then continues to step 630, where the nextlog line 512 in the log file 510 is read into the log buffer 600. For alog parser module 210 that is configured to process static log files510, step 630 is accomplished using standard file access library calls.

[0083] The control routine then proceeds to step 640, field spacers areidentified in the log buffer 600 and marked. The field spacers could bespaces, tabs, commas, or anything that can be used as the separatorbetween the fields in the logging format.

[0084] At step 650, the marked field spacers are replaced with a zeroand the appropriate pointer 612 is set to the next character in the logbuffer 600. Although steps 640 and 650 are shown as separate steps forpurposes of illustration, they are preferably performed at substantiallythe same time. Thus, with a single loop and without moving, copying orallocating any memory, the log buffer 600 containing the single log line512 is converted into a series of smaller character strings, eachrepresenting a particular field 602, and with each zero terminated.

[0085] The pointers 612 in the pointer array 610 can then be used toaccess the fields 602 as if they were separate strings. Accordingly,with minimal processing and absolutely no iterative memory allocation,each log line 512 is read and efficiently separated into its fields 602.

[0086] Real-Time Control Routine for Log Parser Module (210)

[0087]FIG. 5 is a flowchart and schematic diagram of a preferred controlroutine for the read line step of FIG. 4, for accessing and processinglog file data in real time. A web server 500 under normal configurationis shown. The web server 500 handles all requests as they come in andlogs each hit to the log file 510 by appending the log file 510 withdata from each request.

[0088] The built in log file 510 acts as a buffer. It is the simplestand most robust way to pass data between the web server 500 and the livedata access routine 700. The live data access routine 700 can be turnedon or off at will. Once started, the 20 live data access routine 700runs as a low priority daemon. The live data access routine 700 canexist in two states: wait 710 and process 720, toggling between the twoas data arrives into the buffer 510.

[0089] As long as more data exists in the log file 510, the system willstay in the process loop 720. The control routine starts at step 730,where the system checks for an “End of File” mark in the log file 510.As long as this mark is not detected, control moves to read step 740,where the next line in the log file 510 is read into the system. Controlthen continues to the finish control routine step 750, which finishesthe control routine steps in the log parser control routine of FIG. 4,starting with the mark fields step 640 in FIG. 4. All of the read, writeand EOF routines are autonomous, which means the web server 500 cancontinue to write new data to the end of the log 510 during the livedata access routine 700.

[0090] Once the live data access routine 700 catches up and finishes thelog file 510 by reaching the “End of File” marker, control moves totruncate step 760, where the log file 510 is immediately truncated. Thetruncation call sets the size of the log file 510 to zero. Sinceappended files always check file sizes before writing, the next writefrom the web server 500 will automatically start at the beginning of thelog file 510. Control them moves to delay step 770, which delays thecontrol routine for a configurable amount of time (typically <=1second). After this delay interval, control returns to the EOF step 730,where the existence of new data is checked.

[0091] As long the log file 510 is empty, the live data access routine700 will remain in the wait loop 710. In this manner, the live dataaccess routine 700 has real-time access to write data, while maintainingan arms length from the web server 500 itself.

[0092] Website Identification Module (220)

[0093]FIG. 6 is a flowchart and schematic diagram illustrating apreferred control routine for the website identification module 220 ofFIG. 3, which is designed to identify the website that created each logline 512 in a log file 510. The log lines 512 are interleaved andwritten to the log file 510 as hits occur. The format of the log file510 may vary from provider to provider. Some may use the canonicaldomain name in the log file 510, while others will use a subdirectory inthe URI to identify the website.

[0094] There are three configuration variables that pertain to thecontrol routine shown in FIG. 6. The subreport field (SF) specifieswhich field in the log file 510 contains the website identifier text.The subreport expression (SE) is a POSIX extended regular expressionthat is used to capture all or part of the field specified by SF. Thereport name expression (RN) is used to build the website name from theinformation captured by SE.

[0095] As discussed above, the log parser module 210 processes each logline 512 one at a time, and separates the log line 512 into separatefields 602. In the log file 510 shown in FIG. 6, log line field 602′contains the website identifier text, and is also indicated in FIG. 6with shading.

[0096] The control routine for the website identification module beginsat step 800, where log line field 602′ is selected using the SFconfiguration variable. The control routine then continues to step 810,where the subreport expression (SE) is applied to the log line field602′ selected at step 800. This is done using POSIX extended regularexpressions. The operator of the system 100 will need to be familiarwith regular expressions or seek assistance from the manuals ortechnical support. The SE expression is used to match part or all of logline field 602′. Parenthesis are used to define what is to be matched.For example, to simply capture the entire field, the SE expression “(.*) ” would be used. Whereas, to capture the last parts of a “www” domainname, the expression “www\. (. *)” could be used. Whatever is matchedinside the parenthesis is placed into a first variable $1. If there aremultiple sets of parenthesis, then subsequent matched components areplaced into additional variables (e.g., $2, etc.). In the example shownin FIG. 6, two variables, $1 and $2, are used.

[0097] Next, at step 820, the $1 and $2 variables are used to generatethe name 830 of the website. Using the report name expression (RN), thevariables $1 and $2 are replaced with the actual contents of the matchedcomponents. For example, if the following configuration parameters areset:

[0098] SF=2

[0099] SE=SITE:(.*)

[0100] RN=www.mydomain.com/$1

[0101] and the following space-separated log line was processed:

[0102] 123.12.3.1 2000-08-02 SITE:human-resources/index.html 200 1234

[0103] the website identification module 220, at step 800, would select“SITE:human-resources” as log line field 602′ in the log line 512. TheSE would capture everything after the “SITE:” part of log line field602′ as defined by the parenthesis location in the SE expression. Thisinformation is placed into the $1 variable. The website name 830 is thenidentified at step 820 by expanding the RN expression and replacing the$1 variable with the actual contents of the match. In this example, theresulting website name 830 is “www.mydomain.com/human-resources”.

[0104] Visitor Identification Module (230)

[0105]FIG. 7 is a flowchart and schematic diagram illustrating apreferred control routine for the visitor identification module 230 ofFIG. 3. The log file 510 contains a number of log lines 512 or hits.Because the log lines 512 are interleaved, each log line 512 can be froma different visitor. As discussed above, the log parser module 210processes each log line 512 in the log file 510, and places theinformation in the log buffer. The log line fields 602 are separated andthe data is passed to the visitor identification module 230.

[0106] In the log file 510 shown in FIG. 7, log line field 602″ containsthe ID value and log line field 602′″ contains the timestamp of the hit.Log line fields 602′″ and 602′″ are also indicated in FIG. 7 withshading.

[0107] The control routine for the visitor identification module 230begins at step 900, where log line fields 602″ and 602′″ are selected,as represented schematically under the Identify step 900 in FIG. 7. Thecontrol routine then continues to step 910, where the control routinelooks up the ID value 602″ in the visitor hash table 320 of the visitortable 310 (shown in FIG. 2). If the ID value 602″ does not exist in thevisitor hash table 320, control continues to step 920, where a newvisitor record is created in the visitor hash table 320. If the ID value602″ does exist in the visitor hash table, control skips to step 930.

[0108] At step 930, the timestamp 602′″ of the log line 512 is checkedagainst the time range of the visitor record in the visitor hash tablethat corresponds to the ID value 602″. If the timestamp 602′″ fallswithin a predetermined allowable range, control continues to step 940,where the visitor record identified by the ID value 602″ in the visitorhash table is determined to be the existing visitor. Otherwise, controljumps back to step 910, where the seek continues through records notpreviously searched until either a new record is created or anotherexisting visitor is found.

[0109] The Visitor Centric Data Modeling described above has a veryimportant and powerful benefit for real world applications. Many systemsor websites will use multiple servers either mirroring each other oreach handling a different part of a website. Extremely busy websiteswill often use an array of servers to handle the extreme load oftraffic. Other websites may have a secure server area that resides on aspecial machine.

[0110] Whether for robustness or functionality, multiple serverarchitecture is a common practice and appears to create a unique problemfor internet traffic analysis and reporting. Each web server 500 willcreate its own log file 510, recording entries from visitors as theytravel through the website. Often, a single visitor will create logentries in the log file 510 for each web server 500, especially if theweb servers 500 perform different functions of the website.

[0111] It is desirable to be able to merge and correlate more than onelog file 510 so as to have a complete and single record of a particularvisitor. The Visitor Centric Data Modeling described above makes thisability automatic. Since each hit is uniquely identified to a particularvisitor and the timestamp of the hit is recorded, determining the orderand location of the hits do not require any additional engineering. Thesystem and method of the present invention will automatically correlatethe multiple log files as if they were coming from a single log file.

[0112] Buffer Update Module (240)

[0113]FIG. 8 is a flowchart and schematic diagram illustrating apreferred control routine for the buffer update module 240 of FIG. 3.The control routine starts at step 1000, where it is determined if thelog line 512 (hit) is from a new day by analyzing the timestamp 602′″ ofthe log line 512. If the log line 512 is the first of a particular day,then control continues to step 1010. Otherwise, control jumps directlyto step 1020.

[0114] At step 1010, the database buffer 260 is preloaded with anyexisting contents for that day from the actual database 300. Controlthen continues to step 1020.

[0115] At step 1020, the visitor record identified or created by thevisitor identification module 230 is located in the database buffer 260.The located visitor record 1040 is shown schematically under the locatevisitor record step shown in FIG. 8.

[0116] Control then continues to step 1030, where the located visitorrecord 1040 is updated and new information for that visitor is insertedinto the located visitor record 1040. Traffic information is preferablyupdated for the visitor If the located visitor record 1040 is a newvisitor record, then domain, referral, and browser information ispreferably inserted into the located visitor record 1040. All visitorspreferably have their path information updated with any new pageviewinformation. The updated visitor record 1050 is shown schematicallybelow the update record step 1030.

[0117] The timestamp 602′″ of the log line 512 is used to determine theorder of the events that took place. An illustrative example is shown inFIG. 8. In the example shown, a particular visitor is recorded aslooking at Page A 1060 first and then Page C 1070. If the next log line512 processed from the log file 510 indicates that the visitor looked atPage B 1080, the buffer update module 240 (at step 1030) checks thetimestamp 602′″ of the log line 512 to see where in the chain of eventsthe page belongs. In the example shown, Page B 1080 occurred betweenPage A 1060 and Page C 1070. Thus, Page B 1080 is inserted into thevisitor record between the Page A 1060 and Page C 1070. In this manner,the system 100 is able to update and correlate visitor data even if itis out of order in the log file 510.

[0118] This automatic processing of multiple log files 510 came from thediscovery that a single multi-threading web server, such as Netscape,may not log all hits sequentially in time. Due to the nature ofmulti-threading applications, it is possible that a single log file 510may contain hits out of chronological order. The system and method ofthe present invention was therefore designed to handle this situationproperly by checking the timestamp 602′″ of each log line 512 andinserting the information in the log line 512 into the appropriate placein the retrieved visitor record 1040 based on the chain of events. Withthis functionality, the processing of multiple load-balancing log files510 is as simple as reading two log files instead of one.

[0119] The operation of the database buffer 260 will now be explained inmore detail. As discussed above, the log engine 200 contains an internaldatabase buffer 250 that mirrors part of the actual database 300,preferably in RAM. This allows the log engine 200 to correlate andupdate visitor records quickly for each hit without accessing the actualdatabase 300 for each hit. Data is correlated and cached into thedatabase buffer 250, which stores the data temporarily while processingthe log file 510. When processing of the log file 510 is completed, thedatabase buffer 250 is written back to the database 300 in one step.

[0120] The use of a database buffer 250 results in more RAM usage, buthas the advantage of lowering the overhead of database access, resultingin faster processing times. By pre-inspecting the log files 510, the logengine 200 determines the time ranges being used and reads theappropriate data into the database buffer 250. The database buffer 250allows Urchin to avoid reading and writing to the database 300 for eachlog line 512. Instead, the log engine 200 is able to make updates to thevisitor tables 310 and the data tables 315 in memory (through thedatabase buffer 250) and then read and write the entire data block toand from the database 300, which is preferably stored on disk, onlyonce. Database Buffer (250) FIG. 9 is a schematic representation of thecontents of the database buffer 250. As discussed above, the databasebuffer 250 mirrors a portion of the database 300, preferably in RAM.Thus the visitor tables 310′ and data tables 315′ in the database buffer250 have the same format as the visitor tables 310 and data tables 315in the actual database 300.

[0121] Because the database buffer 250 is loaded with data from thedatabase 300, the visitor tables 310′ and data tables 340′ in thedatabase buffer 250 are also relational. The data is centered in thevisitor table 310′, creating a Visitor Centric Data Model. The visitortable 310′ contains a partially filled hash table 320′ that is used forquickly seeking visitor records. Below the partially filled hash table310′, the actual records 325′ contain data about each visitor, such ashits, bytes, time, etc. Each unique visitor will have their own recordin the visitor table 310′. As each log line 512 is processed andidentified to a particular visitor, that visitor's record is updated inthe visitor table 310′ within the database buffer 250.

[0122] Like the visitor table 310 in the actual database 300, thevisitor table 310′ in the database buffer 250 is relational in natureand has a relations area 330′ that contains pointers 335′ to the datatables 315′. Like the data tables 315 in the actual database 300, eachof the data tables 315′ in the database buffer 250 store differentvisitor parameters such as domain, browser, and referral.

[0123] Each data table 315′ contains a hash table 340′, a rank table345′, a record table 350′, and a string table 355′. The hash table 340′is used to seek records in the record table 350′. The rank table 345′ isused to keep track of the top entries in the record table 350′ based onthe number of visitors using the parameter associated the data table315′. This is useful for quick access to reports. The record table 350′stores the actual records within the data table 315′ including thetraffic information associated with the parameter associated with thedata table 315′. The record table 350′ does not store the value of theparameter. Instead, the record table 350′ contains a pointer to a recordin the string table 355′. Each of these subtables (320, 325, 330, 340,345, 350, 355) has fixed width records allowing for efficient reading,writing, and copying of the entire data sets. In addition to the fixedwidth nature of the subtables, the records in the subtables areallocated in large blocks. Memory allocation is not necessary for eachnew record individually.

[0124] Besides using efficient hashing algorithms for processing thedata, resizing of the database buffer 250 is done so that data tables315′ and the hash table 320′ in the visitor table 310′ are partiallyempty. This allows new records to be created instantly withoutallocating additional memory. The gray areas in the data tables 315′ andthe hash table 320′ in the visitor table 310′ indicate the usedportions. As the tables reach a predetermined fullness threshold, theyare preferably increased in size.

[0125] Once the processing of the log file 510 is complete, the datatables 315′ and the visitor table 310′ are written back into the actualstored database 300. The subtables are written separately so that emptyrecords are not stored on the disk that holds the actual database 300.However, the fixed width nature of the subtables allows for efficientwriting of entire blocks of data to the actual database 300. The use ofthe database buffer 250 increases the speed of the log engine 200 byavoiding frequent memory allocation and disk access. By cachinginformation in volatile memory (in the form of the database buffer 250),and reading and writing fixed sized blocks of data, the log engine 200is extremely fast.

[0126] DNS Resolver Module (260)

[0127] When a web server 500 receives a request for a web page, the webserver 500 can either log the IP address of the visitor or it can useDNS to resolve the host and domain information of the visitor. Whiledomain information is valuable for market analysis purposes, theresolution can add significant overhead to the web server 500 and delaythe response of the web server to the end user. It is thereforedesirable to pass the task of DNS resolving onto the system 100 of thepresent invention. This allows the web server 500 to stay as light andquick as possible for visitors accessing the website.

[0128] One of the biggest and most time consuming tasks in processingweb server logs files 510 and creating valuable reports is theprocessing of the reverse DNS of the IP numbers. Each IP number must beconverted to a host/domain name by using the distributed DNS system ofthe Internet. While the local name server may cache many of the answers,most will likely need to go out to the Internet for resolution.

[0129] The speed and scalability of the present system 100 is one of itsadvantages within the operations of large hosting companies. Whetherprocessing single large websites or hundreds of thousands of smallwebsites, the speed of the DNS resolver module 260 is important. The DNSresolver module 260 uses several innovative techniques for improving thespeed and accuracy of the process, as will be described in more detailbelow.

[0130] For each IP number that needs resolving, a query is sent out tothe Internet, where it bounces around a few times in the DNS systembefore coming back with the answer. This can take up to a couple ofseconds, and sometimes the answer never comes back. As far as the localsystem is concerned, the bulk of this time is spent waiting for theresponse. An aspect of the present invention is the discovery that,since each of the queries is separate and unique, the processing can bedone in parallel using multithreading techniques. The overall waitingcan be done all at once instead of sequentially, thus shortening theoverall processing considerably.

[0131] For example, if ten queries are each resolved in one second each,normal overall processing time would be ten seconds. However, by makingthe operation parallel so that all ten queries are processedsimultaneously, then the overall processing time could be reduced to onesecond.

[0132] In practice, however, multithreading systems, such as those basedon the use of POSIX threads and BIND 8.2, carry a significant overhead,and the setting up of sockets and memory locking reduces the benefits ofthe multithreading. Instead, the DNS resolver module 260 is not based onthreads, but takes on the advantage of the parallel nature of theunderlying protocols themselves to simulate threading operation withoutthe additional overhead. Besides improving the overall speed andaccuracy, the porting of the software is simplified, as it depends onless library calls.

[0133] The DNS resolver module 260 generally uses the User DatagramProtocol (UDP) on top of the IP network protocol. The UDP protocol hasinherent parallel capabilities. Each query in the protocol is sent likea letter and uses a connectionless socket. Thus, multiple queries can besent simultaneously without waiting for responses. Multiple responsescan be received at any time and in any order. There is no guarantee thatall the answers will return or that they will appear in any particularorder. But, as long as the queries are tracked with an ID number, thisUDP protocol can be used effectively to parallelize the DNS resolvingoperation without the overhead of threads.

[0134]FIG. 10 is a schematic diagram of illustrating the operation ofthe DNS resolver module 260. The DNS resolver module 260 communicateswith a local name server 1100. The local name server 1100 is part of theInternet 1110 DNS system, but resides in the local network as a primarycacheing name server acting as a relay between the DNS resolver module260 and the multiple DNS servers in the Internet 1110.

[0135] The communication between the DNS resolver module 260 and thelocal name server uses several UDP sockets 1120. The UDP sockets 1120are setup and destroyed only once. Once the UDP sockets 1120 areestablished, the DNS resolver module 260 sends groups of queries 1130.The queries 1130 are represented by “Q” boxes, and the responses (oranswers) 1140 are represented by “A” boxes. The local name server 1100relays the queries 1130 and answers 1140 to the Internet 1110 using abuilt-in DNS system. The local name server has cacheing ability and willremember recently asked queries 1130 and answer immediately instead ofsending them on to the Internet 1110.

[0136] One of the keys to shortening the processing time is to get asmany queries 1130 out in the Internet 1110 at one time. This shortensthe waiting significantly. Without the use of threads, the DNS resolvermodule 260 takes advantage of the UDP protocol, and goes through a loopof sending and reading queries 1130 and answers 1140, as will bedescribed in more detail below. Without waiting for all answers 1140 toreturn or for thread controls to be freed up, the DNS resolver modulepreferably sends as many queries 1130 as possible out into the Internet1110.

[0137] As incoming answers 1140 are decoded and the ID numbers arematched with the originating queries 1130, the IP numbers areefficiently resolved in a manner that truly parallelizes the waiting andthus dramatically reduces the processing time without the overhead ofthreads.

[0138] During the flood of queries 1130 and answers 1140, the DNSresolver module 260 goes through a primary loop of sending queries 1130and reading answers 1140. The kernel level sockets and the local nameserver 1100 can only handle so many requests simultaneously, and willdrop excess queries 1130 if capacity is reached. While having a few(i.e., less than 10%) of the queries 1130 dropped is acceptable, havingtoo many queries 1130 dropped will result in a large percentage ofretries, creating additional work and actually slowing the overallprocessing time. However, it is desirable to send queries 1130 asrapidly as possible. What is needed is a feedback loop that can adjustthe rate at which queries 1130 are sent and the waiting time for answers1140.

[0139]FIG. 11 is a flowchart and schematic diagram of a feedback loopcontrol routine preferably used by the DNS resolver module 260. Aresolver loop 1150 controls a loop that cycles between sending andreading queries 1130 and answers 1140.

[0140] The control routine starts at step 1160, where a group of queries1130 are sent through the UDP sockets 1120. Once the queries 1130 aresent, control continues to step 1170, where the resolver loop 1150 willtry reading answers 1140 for a predetermined amount of time (Timeout).Once the Timeout is reached, the resolver loop will compare how manyqueries 1130 were sent against how many answers 1140 were received, andadjust the Timeout accordingly. Control then returns to step 1160.

[0141] In addition to the socket speed capabilities, certain queries1130 will inherently take longer than others. Some queries 1130 may needto go halfway around the world before resolving is completed. Tominimize this effect, The resolver loop 1150 preferably begins with avery aggressive (short) Timeout, and progressively increases the Timeoutto wait for the answers 1140 that are taking longer to arrive. Theresolver loop 1150 will actually go through multiple loops and, at aslower pace, reattempt queries 1130 that were never answered. Thisadaptable resolving speed control gives the DNS resolver module 260 theability to process the bulk of queries 1130 very quickly, and minimizethe impact of a few slow or non-responding answers 1140.

[0142] The DNS resolver module 260 is preferably configured with theability to increase the resolving percentage and overall accuracy of theDNS resolving module 260 by adapting the query level. Under normal DNSresolving, the IP number is mapped to a specific hostname. For example,the IP number 202.110.52.16 may map to the hostname:

[0143] dial 141-sddc2.npop43.aol.com

[0144] While it may be interesting to see the “dial141-sddc2.npop43 ”part of the hostname, one is typically only interested in the domainpart (e.g., “aol.com”™) of the answer 1140. The first part of the answer1140 is specific to each provider and does not contribute to thedemographic-type reporting that the present system 100 is preferablydesigned to provide.

[0145] In many networks, especially government, military, and smallprivate networks, individuals IPs are not always mapped to anything. Thequery 1130 of a specific IP may return with an answer 1140 of “unknownhost”, which means that not all if the IPs were mapped back to thehostnames. Unfortunately this can reduce the resolving percentage by 20or 30 percent, and skew the demographic data away from non-resolvablenetworks such as are often found in government, military, andeducational networks.

[0146] To make up for this deficiency, the DNS resolving module 260preferably deploys an adaptable resolving level mechanism that attemptsto find out who controls the network in question if the hostname answer1140 returns unsuccessfully.

[0147]FIG. 12 is a schematic diagram of how a preferred embodiment ofthe adaptable resolution mechanism operates. An unresolved IP number1180 enters the DNS resolver module 260. The DNS resolver module 260will make multiple attempts at resolving the IP number by sending outmultiple queries 1130 one at a time using different query information.The first query 1130 a will attempt to resolve the entire specific IPnumber. If that returns unsuccessful, then a second query 1130 b willattempt to resolve the Class-C network address (a Class-C networkaddress is equivalent to the first three parts of an IP address).

[0148] If the second query returns unsuccessful, a third query 1130 cwill attempt to resolve the Class-B network address. If the third queryis unsuccessful, a fourth query 1130 d will attempt to resolve theClass-A network address. Many times, the Class-C or Class-B networkaddresses will resolve correctly when the IP address did not.

[0149] This technique improves the resolving accuracy dramatically andimproves overall performance speed. In the case of government, military,educational and other private networks, “unresolved” percentages havebeen observed to go from 35% down to 8%, and “k12.us” and “navy.mil”show up in the top domains reports using the adaptable resolving levelmechanism of the present invention. While these domains are notresolving their individual IPs, the general source of the traffic isobtained.

[0150] Using the above-described techniques, the DNS resolver modulecomprises a nested-loop, adaptable system that is fast and efficient.The nested-loop architecture is shown in FIG. 13, which is a flowchartof a preferred control routine for the various loops within the DNSresolver module 260.

[0151] The control routine begins by initializing some variables,including five configuration variables 1190 that include:

[0152] resolution target (RT);

[0153] number of loops (NL);

[0154] queries per write (NQ);

[0155] interquery delay (DQ); and

[0156] wait timeout (WT).

[0157] These five settings represent starting points for operation. Theymay be modified at runtime using the feedback mechanism discussed abovein connection with FIG. 11. The control routine comprises a main loop1200, a visitor loop 1210 nested within the main loop 1200, and a readloop 1215 nested within the visitor loop 1210. Dashed lines indicateasynchronous non-loop flow tasks. Sockets are initialized before themain loop 1200 begins.

[0158] The control routine begins at step 1220, where it is determinedif the loop should continue. The loop 1200 will continue as long as the“number of loops” (NL) has not been reached and the “resolution target”(RT) has not been reached. NL is incremented once the loop begins and RTis adjusted after each “decode answer” step 1290, which will bedescribed below.

[0159] The NL and RT variables serve an important purpose. They allow ahigh resolving target to be set, while setting an ultimate timeout.Depending on the size of the data, the number of sites, and the amountof time available, system administrators can modify these variablesbefore operation. Once the resolution target, or the number of loops NL,is reached, the control routine will exit and clean up.

[0160] If NL and RT have not been reached, control continues to thevisitor loop 1210, whose purpose is to build and send queries for eachunresolved visitor in the visitor table 310′. The visitor loop 1210starts at step 1230, where the next unresolved visitor record from thevisitor table 310′ is pulled and a query 1130 is built. An ID number1250 from the visitor table 310′ is used in the building of the query1130 so that it can be tracked later on as a response.

[0161] Next, at step 1240, the query 1130 is sent to the UDP sockets1120. The UDP sockets 1120 are used in round robin fashion which allowsminimizes the waiting for buffer controls.

[0162] A counter keeps track of how many queries 1130 have been sent inthe current batch. Control then continues to step 1260, where thecounter is checked against the NQ variable. If NQ has not been reached,control loops back to step 1230. An optional interquery delay (DQ) step1270 can be inserted between steps 1260 and 1230 to keep the visitorloop 1210 from running too fast.

[0163] If NQ has been reached, which occurs when all the queries in thebatch have been sent, NQ is reset and control then continues to the readloop 1215. The read loop 1215 continues until the WT timeout variable isreached.

[0164] At step 1280, any buffered incoming answers 1140 are read fromthe UDP sockets 1120. Next, at step 1290, each answer 1140 is decoded.Control then continues to step 1300.

[0165] At step 1300, it is determined if the answer 1140 is successful.If the answer 1140 is successful, control continues to step 1310, wherethe visitor table 310′ is updated with the domain information. Controlthen continues to step 1330.

[0166] If, at step 1300, it is determined that the answer 1140 isunsuccessful, control continues to step 1320, where the record in thevisitor table 310′ is modified by changing the resolution status. Theresolution status is used to control the resolution level, as discussedabove. If the answer 1140 comes back as “unknown” then the resolutionstatus is changed for that visitor record, indicating that the nextquery 1130 should attempt to resolve the larger network instead of thespecific IP. Control then continues to step 1330.

[0167] At step 1330, the read loop 1215 condition is checked bydetermining if the incoming UDP sockets 1120 are empty and if thetimeout WT has been reached. If the incoming UDP sockets 1120 are emptyand the WT timeout has been reached, the read loop 1215 ends, andcontrol flows back to the visitor loop 1210 at step 1340. Otherwise, theread loop 1215 continues, and control loops back to step 1280.

[0168] At step 1340, it is determined if the resolution target (RT) hasbeen reached. If it has, the visitor loop 1210 ends, and control flowsback to the main loop 1200 at step 1350. Otherwise, the visitor loop1210 continues at step 1230 with the next batch of unresolved queries.

[0169] At step 1350 of the main loop 1200, the WT timeout is adjusted(increased for the next loop). Control then continues to step 1220,where NL and RT are checked, NL is incremented and starts the entireprocess over again if neither NL nor RT have been reached.

[0170] With minimal overhead, the DNS resolver module 260 takesadvantage of the UDP protocol and maximizes the parallelization of theprocessing. Through a series of nested loops and control parameters, theDNS resolver loop is able to adapt both speed and level in order to meetthe resolving target as quickly as possible. Multiple rounds and levelsof queries 1130 are resent to cover lost or failed attempts, therebyincreasing overall accuracy and resolution percentage dramatically.Thus, system administrators can put a cap on overall processing time,while maintaining a high resolution target.

[0171] Database Update Module (270)

[0172] Once the log file processing is complete and all the log lines512 (hits) are represented in the visitor table 310′ on the databasebuffer 250, the visitor table 310′ is sorted (if multiple websites arerepresented). The database buffer 250 is outputted to the database 300using the database update module 270.

[0173]FIG. 14 is a flowchart and schematic diagram illustrating apreferred control routine for the database update module 270. Theschematic diagram below the control routine steps illustrates what isoccurring to the data during the control routine.

[0174] The control routine starts at step 1360, where the visitors inthe database buffer 250 are sorted based on their associated websiteidentification. Preferably using a quicksort algorithm, the records inthe database buffer 250 are sorted into groups that belong to the samewebsite. If only one website is represented by the log file 510, thenstep 1360 is trivial. However, in the case of multiple websites, thedatabase buffer 250 is sorted into groups of visitors.

[0175] The control routine then continues to step 1370, where thedatabase 300 is opened. Then, at step 1380, the database 300 is updatedwith the data in one of the visitor groups 1400. The process thencontinues to step 1390, where the database 300 is closed.

[0176] The control routine then loops back to step 1370, and thedatabase update process is repeated for each visitor group 1400. Byprocessing the records in groups, the overhead created by accessing thedatabase 300 is reduced.

Database (300)

[0177]FIG. 15 is a schematic diagram illustrating the main components ofthe database 300. As discussed above, the database 300 contains avisitor table 310 and data tables 315. The structure is relational innature as the visitor table 310 relates to information stored in thedata tables 315.

[0178] The database 300 also includes methods module 1410 that providesan interface for accessing, seeking, and inserting data into the visitorand data tables 310 and 315. Both the log engine 200 and the reportengine 400 access the methods module 1410.

[0179] The methods module 1410 is the only module that is allowed todirectly access the data in the database 300. This creates a modularityto the database 300, in which the format of the visitor table 310 and/orthe data tables 315 can be modified without changing the interface tothe other modules in the system 100.

Report Engine

[0180] As ISPs add thousands of web sites to a single system, thecreation of reports can begin to take as long as processing the data.With an ever increasing number of reports to create, the disk space andtime needed to accomplish this side of the task can become a problem.The report engine 400 provides a centralized system that contains asingle copy of the report templates and icons needed to generatereports, and delivers specific reports for a particular web site onlywhen requested.

[0181] The report engine 400 only stores the data for each web site, andnot the specific reports. Since the reports are web-based, they can bedelivered on the fly as requested through the Common Gateway Interface(CGI) of the web server.

[0182]FIG. 16 is a schematic diagram of a preferred embodiment of thereport engine 400. The report engine 400 comprises a session parsermodule 1420, an authentication module 1430, a data query module 1440, anformat output module 1450 and a template/dictionary module 1460.

[0183] In operation, a report request 540 received by the web server 520from an end-user is sent by the web server 520 to the report engine 400through the Common Gateway Interface (CGI) 1470 of the web server 520.The CGI 1470 is a standard mechanism for web servers to allow anapplication to process input and deliver content dynamically via theweb.

[0184] The session parser module 1420 reads the input from the reportrequest 540 and sets internal variables accordingly. The variables arethen used to determine the data to use, the report to create, and theformat of delivery.

[0185] The authentication module 1430 verifies that the end-user thatsent the report request has permission to view the requested report.Upon verification, the data query module 1440 queries the database 300for the raw data needed to generate the requested report.

[0186] The raw data is passed to the format output module 1450, whichuses a set of templates from the template/dictionary module 1460 toformat and create the report 550 to be sent back to the end-user via theweb server 520. The use of templates and dictionaries in the templatemodule allows for easy customization of the reporting format. Templatescan be used to change branding and the overall look and feel of thereport interface. Dictionaries in the template/dictionary module 1460can be used to change the report language on the fly. The end-user cantoggle which dictionary is used for reporting directly through the CGIinterface 1470.

[0187] The access and delivery of reports is preferably controlled usinga Javascript application, which is preferably delivered to the end-userupon the first report request 540. The Javascript Application providesthe mechanisms for displaying report content and querying for newreports.

[0188] The operation of each of the modules in the report engine 400will now be explained in more detail.

Session Parser Module (1420)

[0189] The session parser module 1420 is used to read and access dataspecific to the type of request being made. Furthermore, hostingoperations are creating control panel interfaces with which customerscan login and access all of their tools and applications from oneweb-based location. Customers login once into the control panel, andthen have access to e-mail, website builder tools, newsgroups, etc.

[0190] In order to integrate the present system 100 into custom controlpanel interfaces, the session parser module 1420 is a flexible sessionsensitive system that allows the present system 100 to work seamlesslywith the user's control panel.

[0191]FIG. 17 is a flowchart and schematic diagram of a preferredcontrol routine for the session parser module of FIG. 16. User requestsfor reports are generated and passed to the report engine 400 from theweb server 520. Since the system 100 only contains one report engine400, parameters 1500 are passed to the session parser module 1420 withinthe report engine 400 in order to determine which report to generate.The passing of parameters 1500 is built into the navigation of thereporting interface, i.e., as the end-user clicks through the navigationmenus within the interface and selects a report, the proper parameters1500 are automatically sent to the session parser module 1420.

[0192] The parameters 1500 preferably contain three parts. Thesession-id 1510 is used to keep track of which user is logged into thesystem. The application data 1520 contains the report-specificparameters used to select the correct report. The user session info isan optional set of parameters that can be used to integrate the system100 into a user control panel containing multiple applications.

[0193] The control routine 1420 begins at step with the read input step1540, which parses the list of parameters 1500 and separates the datainto “name-value pairs.” Control then passes to the identify variablesstep 1550, which uses a pre-determined configuration 1560 to match theexternal name-value pairs with internal variables. This allows thesystem 100 to recognize custom variables being used by proprietarycontrol panels and other user interface mechanisms.

[0194] Authentication Module (1430)

[0195]FIG. 18 is a flowchart of a preferred control routine for theauthentication module 1430. After the specific variables of the reportrequest and session are determined, the authentication module 1430provides a flexible way to check access authorization for reportrequesters. While the authentication module 1430 may user either builtin functionality or access pre-existing user databases, the basic stepsof the control routine are the same.

[0196] The control routine starts at step 1600, where the identity ofthe user, the website and the report being requested are determinedbased on data from the session parser module 1420. The control routinethen continues to step 1610, where the validation of the user isperformed.

[0197] Based on configuration, step 1610 can either access internalconfiguration parameters, listing users and reports, or it can access anexternal source (not shown) for user validation. If the user isvalidated for the report request, then control continues to step 1630,where the report request is passed to the data query module 1440. If thevalidation fails, control jumps to step 1640, where an error response isreturned to the user.

[0198] Data Query Module (1440)

[0199]FIG. 19 is a flowchart of a preferred control routine for the dataquery module 1440. This data query module 1440 accesses the methodsmodule 1410 in the database 300 in order to receive a report-ready rawdata set.

[0200] The control routine starts at step 1650, where the identificationof the requested report and other parameters parsed previously by thesession parser module 1420 are formatted into a query that can be passedto the database 300. The format of the query is based on thespecification of the methods module 1410 in the database 300. Typically,SQL type queries are created at step 1650.

[0201] Next, at step 1660, the query generated at step 1650 is sent tothe database 300. Then, at step 1670, the data from the database 300 isreceived and stored in a buffer. The buffer now contains the rawunformatted data for the requested report. Control then continues tostep 1680, where the data received and stored in the buffer is passed tothe format output module.

[0202] Format Output Module (1450)

[0203]FIG. 20 is a flowchart of a preferred control routine for theformat output module 1450. The control routine starts at step 1690,where templates and dictionaries are obtained from thetemplate/dictionary module 1460. The templates and dictionaries arechosen based on the type of report and language desired.

[0204] Control then continues to step 1700, where the requested reportis formatted by merging the data stored in the buffer by the data querymodule 1440 with the chosen templates and dictionaries. Variables arereplaced with values, and words are replaced with dictionary entries.The result is a web-based report ready for delivery custom created foreach user. The report is delivered to the user at step 1710.

[0205] Javascript System

[0206] The report engine 400 preferably uses a Javascript systemcomprising a special combination of HTML and Javascript to produceinteractive reports that are extremely efficient and easy to use. Thebasic concept is that the Javascript, which is loaded into the user'sweb browser contains the code necessary to create the visual reports.Once loaded, the web server 520 only needs to deliver data to the webbrowser, which is then rendered on the user side of the Javascriptsystem.

[0207] The benefits of Javascript system are less connections to the webserver 520. The user can experience real-time navigation, as many of thecontrols do not require new connections to the web server 520. Openingmenus and sorting data occur directly in the web browser. Used inconjunction with the CGI Reporting technology described previously, theJavascript system is extremely efficient and scalable for even the mostcrowded web server communities.

[0208]FIG. 21 is a schematic diagram of a preferred embodiment of theJavascript system. The system comprises an end-user web browser side1810 and a server side 1820.

[0209] When the end-user first accesses the report engine 400, thereport request is sent to the web server 520 which returns theframeset/application 1830 and icons 1840. A Javascript application 1850resides hidden in the parent frameset 1860. The Javascript application1850 then draws the two frames: the navigation frame 1870 and the reportframe 1880. The navigation frame 1870 is drawn directly from theJavascript application 1850.

[0210] As the end-user wants to see a different attribute of the reportor data, they can click on navigational and control elements in eitherthe navigation frame 1870 or the report frame 1880. These controlelements affect variables in the code of the frameset 1860, which thenredraws the necessary subframes. If the end-user has selected somethingthat requires a new data set, only the data is requested and deliveredfrom the web server 520 through the report engine 400. The Javascriptapplication 1850 loads the new data 1890, and draws the subframes andreports accordingly.

Real-time Reporting

[0211] The demand for real-time reporting comes from many sources. Intoday's fast-paced economy, marketing and advertising managers wish tomake rapid decisions and have immediate access to data as it occurs.Likewise, webmasters and system administrators, who are charged withmanaging critical website systems and servers, need real-time monitoringtools in order to keep a finger on the pulse of their systems. Theability to monitor activity in real-time gives the system administratorsthe ability to react to problems and potential attacks. Likewise,managers can monitor marketing strategies and ad campaign effectivenessas they are released.

[0212] As described previously, the system 100, using the live dataaccess control routine shown in FIG. 5, has the ability to record webtraffic into the database 300 continuously as it occurs. Since, asdescribe above, the report engine 400 creates reports when they arerequested, all reports can display up-to-date real-time information. Inaddition to general demographic and statistical reports, the system 100is preferably configured to create a series of reports that arespecifically designed to take advantage of real-time data.

[0213] Visitor Monitor

[0214]FIG. 22 illustrates an example of a visitor monitor report 1900created by the system 100 of the present invention. The report 1900preferably uses custom templates specifically designed for real-timereporting. The report 1900 is a web-based interface that provides a“live” real-time look at one of several possible data parameters 1910,such as visitors, pages, hits, bytes and dollars. The report preferablyincludes a visitor monitor graph 1920 that is preferably refreshedapproximately every second to reflect new data. The data in the visitormonitor graph 1920 preferably moves from right to left as timeprogresses. The current time 1930 is preferably indicated above thevisitor monitor graph 1920. In addition to the graphical display, thereport 1900 preferably displays the current value 1940 of the dataparameter 1910 currently being displayed, as well as the parameter'saverage value for that day 1950.

[0215] By monitoring the visitor data parameter 1910, the currenttraffic level can be monitored as it occurs. Controls 1960 arepreferably provided that are configured so that the user can look atprevious data, stop and freeze the graph, or continue with current data.

[0216] A small amount of Javascript is preferably used to control therefreshing of the visitor monitor report 1900. In addition, the visitormonitor report 1900 preferably uses a small amount of Javascript to timeand reload the image 1970. The image 1970 is generated by the reportengine 400, and uses the PNG format for compact lightweight operation.Since only the image 1970 is reloaded approximately every second, thevisitor monitor report 1900 does not flicker when viewed with mostbrowsers, thus creating an animated appearance to the graph 1920.

[0217] Temporal Visitor Drill Down

[0218] The images 1970 loaded into the visitor monitor report 1900preferably include an HTML/javascript image map that provides“clickable” drill-down access to detailed information within the visitormonitor graph 1920. The visitor monitor report 1900 preferably containsa series of invisible rectangles (not shown) which cover the surface ofthe visitor monitor graph 1920. When the end-user clicks within thevisitor monitor graph 1920, within one of the rectangles, that rectangleis mapped to a specific point in time. This time information is thencompiled into a URL query and sent to the server to provide informationon that specific point in time.

[0219]FIG. 23 is an example of a temporal visitor drill down report 2000created by the system 100 of the present invention, for displaying thetime-specific data discussed above. All visitors 2010 that werecurrently active on the website at the selected time are listed by IPaddress and sorted based on the number of hits 2020. Bytes 2030,pageviews 2040, and length of visit 2050 are also preferably shown foreach visitor 2010. The totals 2060 of bytes 2030, pageviews 2040, hits2020 and length of visit 2050 for all visitors are also preferablydisplayed at the bottom of each column.

[0220] Administrators can use this drill down capability to quicklyassess which visitors 2010 are responsible for the corresponding webserver traffic. Hostile attacks from robots and web spiders can also bemonitored in real-time. Administrators can take action against hostileclients by blocking their access to the servers.

[0221] Visitor Footprint

[0222] In addition to monitoring web server usage, the drill downcapability described above is taken one step further. Each visitor 2010listed in the Temporal Visitor Drill Down report 2000 is preferablyselectable and linked to provide a visitor footprint on that specificvisitor. All of the views are web-based and linking is preferablyaccomplished using simple HTML and Javascript. When the user selects alink on their browser, a new browser window opens and queries the reportengine 400 for the specific information on that visitor.

[0223]FIG. 24 illustrates an example of a visitor footprint report 2100created by the system 100 of the present invention. The visitorfootprint report 2100 preferably contains detailed information on theactivity of the selected visitor, including traffic information 2110,browser information 2120, referral information 2130, domain information2140 and the visitor path 2150 (the specific path the visitor tookthrough the web site).

[0224] If the visitor shown in the visitor footprint report 2100 isresponsible for an e-commerce transaction that is processed by thesystem 100, then additional e-commerce information 2160 is preferablyshown in the visitor footprint report 2100. If the visitor shown in thevisitor footprint report 2100 looked at multimedia clips that arecaptured by the system 100, then additional streaming information 2170is preferably shown in the visitor footprint report.

[0225] The browser information 2120 is preferably analyzed to see if itmatches a known browser or platform. If the browser is recognized thenan icon of the browser and platform 2180 can be optionally shown as partof the browser information 2120. If the visitor is identified as arobot, then an icon of a robot (not shown) can be optionally shown aspart of the browser information 2120. This can be useful for quicklyidentifying hostile attacks from aggressive robots and spiders which canflood the web servers 500 with requests, creating a slow down inresponse times.

[0226] The visitor footprint report 2100 can provide insight into theusage of the website as well as help analyze specific visitors. Whilethe detailed activity of the visitor can be monitored, the system 100preferably does not record, use, or display any personal oridentification information such as e-mail addresses, names, etc. Eachvisitor, while specific in the database 300, preferably remainsanonymous.

[0227] System Meter

[0228]FIG. 25 illustrates an example of a system meter report 2200created by the system 100 of the present invention. The system meterreport 2200 is similar to the web-based visitor monitor report 1900shown in FIG. 22. However, instead of providing a full-sized analysistool, the system meter report 2200 is designed to be small enough to fiton a desktop computer screen at all times.

[0229] The system meter report 2200 contains multiple thumbnail sizedreport images (2210, 2220, 2230, 2240, 2250) that all refresh in thesame manner as the visitor monitor report 1900. To access the systemmeter report 2200, the end-user preferably selects a collapse button1980 (shown in FIG. 22) or a “system meter” navigation button (notshown) within the visitor monitor report 1900. When the system meterreport 2200 is requested from the visitor monitor report 1900, thewindow containing the visitor monitor report 1900 preferably closes anda new smaller window appears on the desktop computer screen containingthe system meter report 2200.

[0230] The system meter report 2200 is preferably configured so that auser can resize the system meter report 2200 (with, for example, acomputer mouse) creating a compact live web-meter that gives themconstant monitoring of critical systems. The system meter report 2200 isalso preferably configured so that selecting one of the report images(2210, 2220, 2230, 2240, 2250) re-opens the full-sized visitor monitorreport 1900.

[0231] The system meter report 2200 preferably displays graphs ofvisitors 2210, hits 2220, pages 2230, bytes sent 2240, and money 2250(if e-commerce is activated).

E-commerce Reporting

[0232] As businesses move from providing passive information about theirproducts to providing interactive shopping capabilities, successfulanalysis of internet traffic can provide valuable information for makingstrategic business decisions.

[0233] In one preferred embodiment of the present invention, Return OnInvestment Reporting (ROIR) technology is used to provide the ability toreport on internet traffic in terms of revenue. All aspects of thevisitor reporting are correlated to dollars spent on the website,providing detailed analysis of when and where revenue is generated.Marketing and advertising managers can use this information to track theeffectiveness of banner ads, the location of and behavior of shoppersand more.

[0234] The key to this technology is the present invention's ability tocorrelate data in a Visitor-Centric way. The Visitor-Centricconfiguration of the present invention allows the system 100 to reporton dollars spent in correlation with any visitor parameter.

[0235] E-commerce websites use shopping cart software (hereinafter“shopping carts”) to provide a secure method for on-line ordering.Shopping carts allow the end-user to add products to their virtualshopping basket, change quantities and check out, similar to a normalshopping experience. There are many commercial shopping cart productssuch as Miva's Merchant™ and Mercantec's Softcart™.

[0236] Whether an e-commerce site uses an off-the-shelf product or acustom engineered application, the concept is the same. The shoppingcart software keeps track of each visitor shopping session. As productsare added to an individual's shopping cart, the software updates thevisitor's specific information. When the visitor decides to check outand purchase the products, the shopping cart provides the necessaryshipping and billing forms and can process the transaction.

[0237] E-commerce Log File Format

[0238] The internet traffic monitoring and analysis system and method ofthe present invention utilizes the e-commerce log files 580 produced bythe shopping carts to perform the e-commerce data correlation. However,the log file formats used by different shopping carts can vary. Apreferred e-commerce log file format for use with the internet trafficmonitoring and analysis system and method of the present invention isdescribed below.

[0239] The e-commerce log file format is preferably a tab-separated,multiline format. The transaction preferably begins with the exclamationmark (!) character (which is thusly prohibited from the rest of thedata). The first line of the e-commerce log file preferably contains thegeographic and overall information on the e-commerce transaction.Subsequent lines preferably contain details on individual products. Thepreferred basic format of the e-commerce log file 580 is as follows:!transfield1 transfield2... productfield1 productfield2... productfield1productfield2... !transfield transfield2... etc.

[0240] Blank fields preferably contain a dash (-) character. Thepreferred format for the transaction line is as follows:/!%{ORDERID}%h%{STORE}%{SESSIONID}%t%{TOTAL}%{TAX}%{SHIPPING}%{BILL_CITY}%{BILL_STATE} %{BILL_ZIP}%{BILL_CNTRY}where %{ORDERID} is the order number. %h is the remote host (seeapache.org). %{STORE} is the name/id of the storefront. %{SESSIONID} isthe unique session identifier of the customer. %t is time in the commonlog format %{TOTAL} is the transaction total including tax and shipping.(decimal only, no “$” signs). %{TAX} is the amount of tax charged to thesubtotal. %{SHIPPING} is the amount of shipping charges. %{BILL_CITY} isthe billing city of the customer. %{BILL_STATE} is the billing state ofthe customer. %{BILL_ZIP} is the billing zip of the customer.%{BILL_CNTRY} is the billing country of the customer

[0241] The preferred format for the product line is:%{ORDERID}%{PRODUCTCODE}%{PRODUCTNAME}%{VARIATION}%{PRICE}%{QUANTITY}%{UPSOLD} where %{ORDERID} is the ordernumber. %{PRODUCTCODE} is the identifier of the product. %{PRODUCTNAME}is the name of the product. %{VARIATION} is an optional variation of theproduct for colors,sizes, etc. %{PRICE} is the unit price of the product(decimal only, no “$” signs). %{QUANTITY} is the quantity ordered of theproduct. %{UPSOLD} is a boolean (1/0) if the product was on sale.

[0242] An aspect of the present invention is the optional provision of aplug-in module for existing shopping carts that will allow the shoppingcart to create the e-commerce file log 580 in the preferred format.

[0243] E-commerce Visitor Correlation

[0244] In order to provide the ROIR reporting described above, thesystem 100 performs a special correlation between the e-commercetransaction data in the e-commerce log file 580 and normal websitevisitor traffic data in the standard log files 510.

[0245] As discussed above, both the standard log files 510 and thee-commerce log files 580 are processed by the log engine 200. Asdiscussed above in connection with FIGS. 3-9, each line of the log files510 and 580 is processed and passes through the following steps. (1) thelog line 512 of the log file 510 or 580 is read into the database buffer250; depending on the format of the log file, the log line 512 isprocessed and identified; (3) the website identification module is usedif multiple websites are logged into the same log file 510 or 580; (4)the visitor identification module uses the IP number and a timestampfound in the log line 512 (or session id) to establish the uniqueidentity of the visitor; (5) the visitor ID is used to determine therecord number in the visitor table 310′; and (6) the record is updatedwith the information from the log line 512.

[0246]FIG. 26 shows the visitor table 310′ in the database buffer 250.As discussed above, the visitor table 310′ may include many fields, suchas Hits 3000, Bytes 3010, Pages 3020, Dollars 3030, Referrals 3040,Domain 3050, Browser 3060, etc. The visitor table 310′ is where thee-commerce correlation is done.

[0247] The e-commerce log file 580 will update the visitor's Dollarsfield 3030, which indicates money spent by the visitor. The remainingfields are updated using the standard log file 510. The Dollars field3030 is used to determine money spent on the website in terms of theother fields (parameters).

[0248] For example, the Referral field 3040 in the visitor table 310′holds a record number to an entry in the referral data table 3070. Thereferral in the referral data table 3070 indicates how the visitor foundthe website. For example, if the visitor came from the yahoo.com™website, then the referral field 3040 in the visitor table 310′ wouldhold the record number pertaining to the yahoo.com™ entry in thereferral data table 3070. All visitors that came from yahoo.com™ wouldhave the same referral record number in the referral field 3040.Similarly, the Domain and browser fields 3050 and 3060 in the visitortable 310′ would hold record numbers to entries in the domain data table3080 and browser data table 3090. The other fields 3000, 3010 and 3020would likewise have data tables associated with them (not shown).

[0249] By looping over the visitor table 310′, a money amount can beassociated with each entry in any of the data tables. If, for example, amoney amount is associated with each entry in the referral data table3070, all shoppers that came from yahoo.com™ (as an example) would beaggregated to produce a return-on-investment indicator.

[0250]FIG. 27 shows an example of an ROIR e-commerce report generated bythe system 100 of the present invention. The report 3100 uses the domaindata table 3080, shown in FIG. 22, to produce a top-10 report ofInternet Domains whose visitors spent the most money on the websiterepresented by the report 3100. In the example report 3100, Aol.com™ isthe top domain in terms of money, spending approximately 46% of allmoney spent on the website.

[0251] The total money spent by all the visitors for each domain isdisplayed when the “Dollars” tab 3110 is selected. The average amount ofmoney spent by each visitor at each domain can also be displayedselecting the “Dollars/Visitor” tab 3120. The average amount of moneyspent by each visitor is calculated by dividing the total amount ofmoney spent at each domain by the number of visitors to the domain.

[0252] E-commerce website owners can use these correlations to makevaluable business decisions. The system and method of the presentinvention can correlate money to keywords, banner ads, search engines,referrals, domains, countries, browsers, platforms, or any otherparameter of interest. The website operators can monitor the performanceof search engine registrations, banner ad placements, regional adcampaigns, and more.

User Interfaces/System Reports

[0253] Examples of preferred user interfaces and system reports willknow be discussed. All reports and interfaces are preferably web-basedand viewed with a web browser. While not all possible reports are shown,the reports shown are representative of the types of reports and reportconfigurations that are possible with the system and method of thepresent invention. Accordingly, it should be appreciated that theconfiguration and types of reports, as well as the configuration andtypes of user interfaces may vary from those shown while still fallingwithin the scope of the present invention.

[0254] Further the user interfaces described below are for generation ofstatic reports. The user interfaces used for real-time reports weredescribed above in connection with FIGS. 22-25.

[0255]FIG. 28 shows a preferred browser-based user interface 4000. Thisis preferably the first user interface 4000 shown when the user firstaccesses the reporting interface of the system 100. The user interface4000, preferably contains areas 4020 and 4030 for displaying productand/or company logos. The user interface 4000 also includes a mainreporting window 4100 for displaying a currently chosen report.

[0256] The user interface 4000 preferably includes a navigation area4040 that contains a collection of menus that group the availablereports into different categories, preferably seven main categories,each with an associated link 4050: Traffic; Pages; Referrals; Domains;Browsers; Tracking; and E-Commerce. A collection of links to specificreports 4060 related to a chosen category link 4050 is preferablydisplayed under a chosen category link 4050. The currently chosen reportlink 4070 is preferably indicated by a change in color Or shading. Inthe example shown in FIG. 28, the currently chosen report link 4070corresponds to the “Snapshot” report.

[0257] The user interface 4000 preferably includes a “date range”functions area 4080. Depending on the report chosen, this date rangefunctions area 4080 allows the user to select the date range of thereport being shown. The user interface also preferably includes acontrols area 4090 that preferably includes preferences and reportexporting features. The preferences function of the controls area 4090allows the user to change report settings, such as the language that isused for display. The exporting function of the controls area 4090allows the user to export the currently viewed data for use in otherapplications, such as Microsoft Excel™.

[0258] The user interface 4000 also preferably includes a HelpInformation area 4130, which gives a brief synopsis of the report beingdisplayed and provides a link 4135 for more in-depth information.

[0259] Traffic Related Reports

[0260] The Snapshot report 4010 shown in FIG. 28 is preferably a bargraph 4110 of the last 7 days of web site traffic in terms of variousfields, preferably Visitors, Pageviews, Hits, or Bytes. There arepreferably tab controls 4120 on the report 4010 that allow the user toselect which field is displayed. The date of each day is preferablyshown below the bars in the graph 4110.

[0261]FIG. 29 shows an example of an Hourly Graph report 4200. TheHourly Graph report preferably shows traffic versus hour of the day interms of various fields, preferably Visitors, Pageviews, Hits, or Bytes.There are preferably tab controls 4120 on the report 4200 that allow theuser to select which field is displayed.

[0262] The Hourly Graph report 4200 is preferably a bar graph indicatingthe 24 hours of the day from left to right. This report allowsadministrators to see when peak activity is expected and when to plansite maintenance and upgrades.

[0263] Other reports available under the Traffic category preferablyinclude the Summary, Daily Graph, Monthly Graph and Top Servers reports.The Summary report gives a text based summary of overall traffic to thesite. The Daily Graph is similar to the Hourly Graph report 4200, exceptthat the traffic is displayed as a function of the day of the month. TheMonthly Graph report provides traffic displayed versus month of theyear, and the Top Servers report indicates which log files or serversare responsible for the most traffic in the cluster.

[0264] Pages Related Reports

[0265]FIG. 30 shows an example of a Top Pages report 4300. The Top Pagesreport 4300 is one of the reports listed under the Pages menu 4310. TheTop Pages report 4300 preferably indicates a top-ten type list, rankingwhich pages in the website are the most visited. The tabs 4120 arepreferably used to view the report 4300 in terms of either Pageviews orBytes transferred. Next and previous buttons 4320 are preferablyprovided that allow the user to scroll through the Top Pages Report4300. The number of entries shown are preferably adjusted with the#Shown menu 4330.

[0266]FIG. 31 shows an example of a Directory Tree Report 4400. TheDirectory Tree Report 4400 is similar to the top pages report 4300 ofFIG. 30, except that the Directory Tree Report 4400 preferably includeslinks 4410 next to each entry that can be selected to open informationbelow that entry. This allows for easy display and navigation ofhierarchical type data, such as a directory structure.

[0267] The directory tree report 4400 indicates which directories withinthe website architecture are being accessed the most. Under eachdirectory, the end user can drill down to see the subdirectories orindividual pages contained within the primary directory by selecting thelinks 4410.

[0268] Other pages-related reports in the Pages menu 4310 preferablyinclude File Types, Status/Errors, and Posted Forms. The File Typesreport is a top-ten type report that indicates which file extensions ortypes are accessed the most. This allows the user to distinguish betweenHTML page, GIF images, etc. The Status/Errors report is a tree-typereport that indicates status codes and error messages that occur duringweb content delivery. The Posted Forms report is a top-ten type reportthat indicates the forms that were submitted using the POST method asdefined in the HTTP protocol.

[0269] Referrals Related Reports

[0270]FIG. 32 shows an example of a Search Engine report 4500 from theReferrals menu 4510 of the navigation area 4040. The Referrals menu 4510provides reports related to how the visitor found a website.

[0271] The Search Engines report 4500 contains a tree-type list of themost used search engines. Each search engine can then be expanded to seewhich keywords were used during those searches.

[0272] Additional reports in the Referrals menu 4510 preferably includeTop Referrals, Top Keywords, and the Referral Tree. The Top Referralsreports is a simple top-ten type list of the top referring URLs. TheKeywords report indicates the top keywords used across all searchengines. The Referral Tree report breaks down the Referral URLs bydomain.

[0273] Domains Related Report

[0274]FIG. 33 is an example of a Top Domains report 4600, whichindicates regional and network information about the visitors. Thevisitor's domain is determined by the IP address of the visitor. Thedomain is resolved using the Reverse DNS module 260 within the logengine 200 described previously.

[0275] Additional reports under the Domains menu 4610 in the navigationarea 4040 preferably include Domain Tree and Top Countries. The DomainTree report provides the different levels of domains. Primary domainssuch as .com and .edu are shown first. Preferably, these can be expandedto show detailed information within. The Top Countries report expandsand analyzes which countries people are coming from.

[0276] Browsers Related Reports

[0277]FIG. 34 shows an example of a Browser Tree report 4700, which is atree-type report that ranks the most widely used browsers by visitor tothe website. Browsers such as Internet Explorer™ and Netscape™ arereported upon as a whole and by version. Each primary browser can beexpanded to see the breakdown by version.

[0278] Additional reports in the Browsers menu 4710 of the navigationarea 4040 preferably include Platform Tree and Top Combos. The PlatformTree report indicates the operating system of the visitor. It is atree-type report that can be expanded to show the versions under eachplatform. The Top Combos report ranks the correlation between browserand platform.

[0279] Tracking Related Reports

[0280]FIG. 35 shows an example of a Top Entrances report 4800. As partof the Tracking menu 4810 within the navigation area 4040, the TopEntrances report 4800 indicates the starting point of visitors in thewebsite. Additional reports in the Tracking section 4810 preferablyinclude Top Exits, Click Through, Depth of Visit, Length of Visit, andUsernames.

[0281] The Top Exits report provides a list of the last page visitorslooked at before leaving the site. The Click Through report indicatesthe click percentage from any one page to another. The Depth of visitreport provides a histogram of the number of pages viewed by visitors.The Length of Visit report provides a histogram of the time spent on thesite by visitors. The Usernames report analyzes the usage of passwordprotected areas of a website by listing the usernames that were used tologin to the those sections.

[0282] E-commerce Related Reports

[0283]FIG. 36 shows an example of a Top Products report 4900, which ispart of the E-Commerce menu 4910 in the navigation area 4040. The TopProducts report 4900 indicates the Top Products purchased from the siteby revenue.

[0284] Additional reports in the E-Commerce menu 4910 preferably includeTotals, Product Tree, Regions, and Top Stores. The Totals report gives asummary of overall e-commerce activity. The Product Tree report groupsproducts by category. The Regions report indicates the regional locationof shoppers including cities, states and countries. If multiple storefronts are used by the same shopping system, the Top Stores report canbreakdown revenue by storefront.

System Integration

[0285] The system and method of the present invention can be configuredin many different ways. From single server configurations to complexload balancing systems, the system and method of the present inventionis flexible in its integration abilities. While it is difficult tocatalog every possible architecture, several possible configurations aredescribed below.

[0286] Webserver vs. Dedicated Server

[0287] The system and method of the present invention can be implementeddirectly on the web server 500 that produces the log files (510, 580),or on a separate dedicated computer. If the system 100 is implementeddirectly on the web server 500, it can then use the web server 500 forthe reporting web server 520. If the system 100 is implemented on adedicated box, then a web server 520 will need to be configured on thededicated computer in order to service the report requests.

[0288] Access to log files is slightly more complicated on a dedicatedcomputer. If the system 100 is implemented on a dedicated computer, thenthe log files (510, 580) from the web server 500 will need to beaccessible to the dedicated computer by using FTP, NFS, or some othersuitable disk access method. Real-time processing of log files requireswriting permission to the log files (510, 580) which may require anextra configuration step if using a dedicated computer.

[0289] As long as the log files (510, 580) are accessible (withpermissions) and a web server is available, the system 100 can work justas well directly on the web server 500 or on a dedicated computer.

[0290] One Website vs. Multiple Websites

[0291] The system and method of the present invention can handlemultiple websites. During integration, a unique reporting directory fordata storage can be configured for each of the websites. The system 100will link the individual report directories back to the maininstallation, so that there is only one copy of the templates and icons.Users will need internet access to the reporting directories. Thus, theweb server 520 configuration should be similar to the system 100configuration. A typical installation will use a subdirectory withineach website's document root to store and access the reports.

[0292] Whether there is one website or many, the integration preferablyprovides a unique web accessible directory for each websiteconfiguration.

[0293] Distributed Logs vs. Central Logs

[0294] Web servers 500 can be configured to create unique log files(510, 580) for each website in the web server's configuration, or asingle log file (510, 580) for all websites in the configuration. Thesystem of the present invention can be configured to work with either ofthese architectures. If each website has its own unique log file, thenthe log files are preferably entered into the system's 100configuration, so that each website has its own area in theconfiguration. The system 100 will process the logs one at a timetreating each website independently.

[0295] If the web server 500 is configured to log centrally, then thelog file (510, 580) preferably contains some website identificationmarker in order for the system 100 to be able to sort and process thelog file 510. As described previously, the website identification module220 is designed to capture some parameter within the log file, in orderto determine which hits go with which websites. This type of integrationcan automatically detect new websites as they are added to the webserver 500 without modifying the configuration of the system 100.

[0296] Single Log vs. Multi-log

[0297] The system and method of the present invention can be configuredfor systems that reside on one web server 500 or on multiple web servers500. Multiple web servers 500 are often used for load-balancing,redundancy, and functional serving. Multiple web servers 500 will eachhave their own set of logs 510. The system and method of the presentinvention can automatically correlate the visitor centric data frommultiple logs (510, 580), as described previously. By simply enteringthe multiple logs in the configuration, the system 100 will process themultiple logs.

[0298] E-commerce vs. No-commerce

[0299] As described previously, the system and method of the presentinvention can include e-commerce reporting functionality, and can beused in conjunction with shopping cart software. The e-commerce logfiles 580 are handled similarly to the multi-log architecture discussedabove. The e-commerce logs 580 are simply treated as multiple logs.Additional entries will need to be made in the configuration.

[0300] For integration into e-commerce systems, the shopping cartsoftware is preferably configured to create the preferred log fileformat described above.

[0301] Control Panel vs. Stand-alone

[0302] Many larger hosting providers are creating centralized web-basedcontrol panels that contain links to all of the tools and systemsavailable to the hosting clients. Hosting clients log into the controlpanel once and are provided with customized information and interaction,such as accessing their unique e-mail account, uploading files to theirunique website, and viewing the reports created by the system of thepresent invention.

[0303] Stand-alone systems will have unique reporting directories foreach website. Thus, accessing the reporting area is simple, as eachreporting area will have a unique URL. Protecting report access can beaccomplished through the web server 520 itself, and does not requireintegration with the system 100.

[0304] For control panel integrations, the system and method of thepresent invention is preferably sensitive to session controllingtechnology. As described previously, the session parser module 1420 hasthe ability to detect custom variables and control report delivery froma central location.

[0305] The various components of the present invention are preferablyimplemented on internet (e.g., web) servers, which may be or include,for instance, a work station running the Microsoft Windows™ NT™,Windows™ 2000, UNIX, LINUX, XENIX, IBM, AIX, Hewlett-Packard UX™,Novel™, Sun Micro Systems Solaris™, OS/2™, BeOS™, Mach, Apache OpenStep™, or other operating system or platform. However, the variouscomponents of the present invention could also be implemented on aprogrammed general purpose computer, a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit elements, an ASIC or other integrated circuit, a hardwiredelectronic or logic circuit such as a discrete element circuit, aprogrammable logic device such as a FPGA, PLD, PLA, or PAL, or the like.In general, any device on which a finite state machine capable ofimplementing the modules and control routines discussed above can beused to implement the present invention.

[0306] While the foregoing description includes many details andspecificities, it is to be understood that these have been included forpurposes of explanation only, and are not to be interpreted aslimitations of the present invention. Many modifications to theembodiments described above can be made without departing from thespirit and scope of the invention, as is intended to be encompassed bythe following claims and their legal equivalents.

What is claimed is:
 1. A system for analyzing and monitoring internettraffic, comprising: a relational database; a log engine that processeslog files received from at least one internet server and stores dataprocessed from the log files in the relational database; and a reportengine that generates reports based on the processed data stored in therelational database.
 2. The system of claim 1, wherein the relationaldatabase comprises a plurality of hash tables.
 3. The system of claim 1,wherein the plurality of tables comprise: a visitor table that storestraffic information generated by a visitor to an internet site hosted bythe at least one internet server; and a plurality of data tables,wherein each data table stores records related to a respectiveparameter.
 4. The system of claim 3, wherein the visitor table comprisesat least one pointer to at least one record stored in at least one ofthe data tables.
 5. The system of claim 3, wherein the respectiveparameters comprise: domain names from which the visitor originated; andweb browsers used by the visitor; and other internet sites that referredthe visitor to the internet site.